One thing that has remained consistent in IT over the years is the difficulty of measuring the productivity and output of transformational projects. Multiple studies confirm that more than 50% of IT related projects were either complete failures or only partial successes. On average, 9.7% to 12.2% of every dollar spent has gone to waste. What’s particularly interesting is that often, these failures are predetermined before the projects even start, primarily due to unrealistic timeline and skills misalignment. Under such constrains, project teams and deliverables have suffered.
The root cause of many project planning malpractices lies in most organizations not having clear visibility into their application blueprints and the broader system architecture. As a result, estimates are often produced based on past experiences and intuitions. Complexity associated with application interdependencies and change impact are often understated. The lack of application understanding also makes resource assignment difficult. Skillset misalignment reduces team productivity and increases project risk. Aside from technical factors, organizations have to satisfy market needs and keep ahead of competitions. No project management or estimation methodologies may change the mandate that certain outcomes must be delivered within a certain time.
One thing organizations may introduce and immediately improve their success rate is Software Intelligence. Software Intelligence provides everyone in the organization with a single record of truth, helping teams objectively measure productivity and communicate with project leadership and sponsors. Risk and value trade-offs are evaluated as part of project planning. With Software Intelligence, technology owners can collaborate with business owners using objective data to define achievable scope, set expectations and pragmatically pivot as the project requires.
Technical teams agree that hidden software complexity plays a major role in project slippage. Developers may find themselves with an application that is relatively easy to understand or a labyrinth with dated documentation. Software complexity and scale of change must be known for the project team to produce an effective plan of attack. With Software Intelligence, complexity measurements such as changeability, transferability and cyclomatic complexity give us clarity on the intricacy of application architecture, dead code volume, algorithmic and SQL query complications. Sizing measurements quantified by function point and technical point provide us the scale of impact that comes along with the application change.
Foundation to better project estimation can be built by understanding the correlation between the effort required to produce a set amount of work and software complexity. This can be accomplished by accumulating data on project tasks, effort, duration and application complexity for each project. The estimated units of measure by various technologies and degrees of complexity will improve as data grow. Software Intelligence will soon be able to identify outliers, anomalies, correlations and predict the outcomes of projects using data organizations already have. Project duration, effort and resource requirement, probability of success and likelihood of delivering on time may all also be calculated.
With this new level of tech maturity, the time is right for Software Intelligence as a tool for improving accuracy and consistency. Software Intelligence gives organizations the ability to collaborate and talk about feasibility versus not knowing the risk and hoping the project gets done in a relatively timely manner.